Virus Variables/Viral Score (Fig 2)

Fig 2c heatmap

Viral Score Calcs

For each timepoint, animal:

  1. Calculate tp_score across all virus variables:

\(\text{tp_score__WeekXX}=\frac{\text{gag_PCR}_{\text{WeekXX }} + \text{ orfI_II_PCR}_{\text{WeekXX }} + \text{ number_of_bands}_{\text{WeekXX }}/\text{number_of_bands}_{\text{MAX }} + \text{ log}_{10}(\text{p24_titer}_{\text{WeekXX}}) /log_{10}(\text{p24_titer}_{MAX})}{TotalNumberOfVirusVariables}\)

  • each virus variable has range of 0-1
  • each tp_score__WeekXX has range of 0-1
  1. Calculate fracVars_notZero across all virus variables:

\(\text{fracVars_notZero__WeekXX}=\frac{\text{NumberOfVirusVariables__NotZero}_{WeekXX}}{TotalNumberOfVirusVariables}\)

  • each fracVars_notZero__WeekXX has range of 0-1
  1. Combine tp_score__WeekXX (step 1.) & fracVars_notZero__WeekXX (step 2.)

\(\text{tp_comb_score__WeekXX}=\frac{\text{tp_score__WeekXX }+\text{ fracVars_notZero__WeekXX}}{2}\)

  • each tp_comb_score__WeekXX has range of 0-1

Then, for each animal:

Generate composite “timepoint-derived” scores:

  1. Average tp_comb_score (from step 3.) across timepoints

\(\text{tp_comb_scores__Mean}=\frac{\sum_{i=Baseline}^{n=Week21}{\text{tp_comb_score}_{i}}}{TotalNumberOfTimepoints}\)

  1. Calculate fracTPscores_notZero (from step 3.) across timepoints

\(\text{fracTPscores_notZero}=\frac{\sum_{i=Baseline}^{n=Week21}{\text{tp_comb_score__NotZero}_{i}}}{TotalNumberOfTimepoints}\)

  1. Combine tp_comb_scores__Mean (step 4.) & fracTPscores_notZero (step 5.)

\(\text{TP_COMBO}=\frac{\text{tp_comb_scores__Mean } + \text{ fracTPscores_notZero}}{2}\)

Now, for each virus variable, animal:

  1. Calculate var_score across all timepoints:

\(\text{e.g. var_score__p24_titer}=\frac{\text{ log}_{10}(\text{p24_titer}_{\text{Baseline}}) /log_{10}(\text{p24_titer}_{MAX}) \text{ } + \text{ log}_{10}(\text{p24_titer}_{\text{Week03}}) /log_{10}(\text{p24_titer}_{MAX}) \text{ } + \text{ ... } + \text{ log}_{10}(\text{p24_titer}_{\text{Week21}}) /log_{10}(\text{p24_titer}_{MAX})}{TotalNumberOfTimepoints}\)

  • each p24_titer has range of 0-1
  • each var_score__p24_titer has range of 0-1
  1. Calculate fracTPs_notZero across all timepoints:

\(\text{e.g. fracTPs_notZero__p24_titer}=\frac{\text{NumberOfTimepoints__NotZero}_{p24_titer}}{TotalNumberOfTimepoints}\)

  • each fracTPs_notZero__p24_titer has range of 0-1
  1. Combine var_score__p24_titer (step 7.) & fracTPs_notZero__p24_titer (step 8.)

\(\text{e.g. var_comb_score__WeekXX}=\frac{\text{var_score__p24_titer }+\text{ fracTPs_notZero__p24_titer}}{2}\)

  • each var_comb_score__p24_titer has range of 0-1

Generate composite “virus variable-derived” scores:

  1. Average var_comb_score (from step 9.) across virus variables

\(\text{var_comb_scores__Mean}=\frac{\sum_{i=\text{gag_PCR}}^{n=\text{p24_titer}}{\text{var_comb_score}_{i}}}{TotalNumberOfVirusVariables}\)

  1. Calculate fracVARscores_notZero (from step 9.) across timepoints

\(\text{fracVARscores_notZero}=\frac{\sum_{i=\text{gag_PCR}}^{n=\text{p24_titer}}{\text{var_comb_score__NotZero}_{i}}}{TotalNumberOfVirusVariables}\)

  1. Combine var_comb_scores__Mean (step 10.) & fracVARscores_notZero (step 11.)

\(\text{VAR_COMBO}=\frac{\text{var_comb_scores__Mean } + \text{ fracVARscores_notZero}}{2}\)

And finally combine “timepoint-derived” & “virus variable-derived” scores:

  1. Combine tp_comb_scores__Mean (step 4.) & var_comb_scores__Mean (step 10.)

\(\text{Mean}=\frac{\text{tp_comb_scores__Mean } + \text{ var_comb_scores__Mean}}{2}\)

  1. Combine fracTPscores_notZero (step 5.) & fracVARscores_notZero (step 11.)

\(\text{fracScores_notZero}=\frac{\text{fracTPscores_notZero } + \text{ fracVARscores_notZero}}{2}\)

  1. Combine TP_COMBO (step 6.) & VAR_COMBO (step 12.)

\(\text{COMBO}=\frac{\text{TP_COMBO } + \text{ VAR_COMBO}}{2}\)

Fig 2d heatmap

timepoint-derived scores from step 3. and their combinations from steps 7-9.

plus

variable-derived scores from step 6. and their combinations from steps 10-12.

plus

combinations of timepoint-derived and variable-derived scores (steps 13-15.)

Fig 2e boxplot

Triple vs No depletion: Day 0 broad cell populations (Supp Fig 2)

red p value: p < 0.05, left timepoint/group is higher than right timepoint/group

blue p value: p < 0.05, left timepoint/group is lower than right timepoint/group

black p value: p > 0.05

HTLV-1ACoI-L vs HTLV-1A_WT: broad cell populations (Supp Fig 4) , including no depletion

only p < 0.05 are shown for clarity

red p value: left timepoint/group is higher than right timepoint/group

blue p value: left timepoint/group is lower than right timepoint/group

Cell subsets Differences between groups (Fig 3, Supp Fig 6, Supp Fig 7)

Fig 3a DCs heatmap

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

BAL
alphabetical

by Dir

Blood
alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Blood free y

Blood same y

Fig 3b Monocytes heatmap

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

BAL
alphabetical

by Dir

Blood
alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Blood free y

Blood same y

Fig 3c Neutrophils heatmap

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots

BAL
alphabetical

by Dir

Blood
alphabetical

by Dir

Scatterplots with box

BAL free y

BAL same y

Blood free y

Blood same y

Cytokine/chemokine Differences between groups (Fig 4, Supp Fig 8, Supp Fig 9)

Fig 4abc Cytokines heatmaps

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Scatterplots with box (Supp Fig 8)

BAL free y

BAL same y

Blood free y

Blood same y

Cytokines: vs No Depletion (Supp Fig 9)

Blood

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

BAL

untrimmed

ROWSCALED

trimmed, p < 0.05

ROWSCALED

Table

Associations between sig different cytokines & cell subsets (Fig 4de)

Animals in correlations: 15P044, 17P008, 17P039, 17P062, 18C266, DG8Z, DG8i, DGX3, DHF6, RA6, RH5, RKF, TMN, TRE, TZW

calculate comparisons between CellSubsets in BAL vs ChiVsWT_Cytokines_inBAL : CCL19xx__week12, CCL3xx__week12, FLT3LGxx__week21, IL15xx__week21, IL18xx__week21, IL1Bxx__week05, IL1Bxx__week21, IL33xx__week21, MMP1xx__week05, MMP12xx__week00

calculate comparisons between CellSubsets in Blood vs ChiVsWT_Cytokines_inBAL : CCL19xx__week12, CCL3xx__week12, FLT3LGxx__week21, IL15xx__week21, IL18xx__week21, IL1Bxx__week05, IL1Bxx__week21, IL33xx__week21, MMP1xx__week05, MMP12xx__week00

calculate comparisons between CellSubsets in Blood vs ChiVsWT_Cytokines_inBlood : CCL13xx__week12, CCL2xx__week12, IL33xx__week12, IL6xx__week05, IL7xx__week00

calculate comparisons between CellSubsets in BAL vs ChiVsWT_Cytokines_inBlood : CCL13xx__week12, CCL2xx__week12, IL33xx__week12, IL6xx__week05, IL7xx__week00

0.001

compile associations with p < 0.001

0.01

compile associations with p < 0.01

Virus in lung (Figure 6)

including TiT

by Lobe, Assay

by Assay, Lobe

excluding TiT

by Lobe, Assay

by Assay, Lobe

Session info

## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.4.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rstatix_0.7.2      janitor_2.2.0      geepack_1.3.10     MESS_0.5.12       
##  [5] ggalluvial_0.12.5  pander_0.6.5       strex_2.0.1        data.table_1.16.2 
##  [9] factoextra_1.0.7   corrplot_0.95      colorspace_2.1-1   RColorBrewer_1.1-3
## [13] officer_0.6.6      flextable_0.9.6    ggplotify_0.1.2    plotly_4.10.4     
## [17] ggrepel_0.9.6      ggbeeswarm_0.7.2   downloadthis_0.3.3 DT_0.33           
## [21] gridExtra_2.3      gtsummary_1.7.2    kableExtra_1.4.0   broom_1.0.7       
## [25] knitr_1.48         ggpubr_0.6.0       dendsort_0.3.4     pheatmap_1.0.12   
## [29] cowplot_1.1.3      lubridate_1.9.3    forcats_1.0.0      stringr_1.5.1     
## [33] dplyr_1.1.4        purrr_1.0.2        readr_2.1.5        tidyr_1.3.1       
## [37] tibble_3.2.1       ggplot2_3.5.1      tidyverse_2.0.0    readxl_1.4.3      
## [41] openxlsx_4.2.5.2  
## 
## loaded via a namespace (and not attached):
##  [1] geeM_0.10.1             rstudioapi_0.16.0       jsonlite_1.8.9         
##  [4] magrittr_2.0.3          farver_2.1.2            rmarkdown_2.28         
##  [7] fs_1.6.4                ragg_1.3.1              vctrs_0.6.5            
## [10] memoise_2.0.1           askpass_1.2.1           htmltools_0.5.8.1      
## [13] haven_2.5.4             curl_5.2.3              cellranger_1.1.0       
## [16] Formula_1.2-5           gridGraphics_0.5-1      sass_0.4.9             
## [19] bslib_0.8.0             htmlwidgets_1.6.4       cachem_1.1.0           
## [22] gt_0.10.1               uuid_1.2-0              mime_0.12              
## [25] lifecycle_1.0.4         pkgconfig_2.0.3         Matrix_1.7-0           
## [28] R6_2.5.1                fastmap_1.2.0           snakecase_0.11.1       
## [31] shiny_1.10.0            digest_0.6.37           textshaping_0.3.7      
## [34] crosstalk_1.2.1         labeling_0.4.3          fansi_1.0.6            
## [37] timechange_0.3.0        httr_1.4.7              abind_1.4-8            
## [40] compiler_4.4.1          fontquiver_0.2.1        withr_3.0.2            
## [43] backports_1.5.0         carData_3.0-5           highr_0.11             
## [46] ggsignif_0.6.4          MASS_7.3-60.2           openssl_2.2.2          
## [49] gfonts_0.2.0            tools_4.4.1             vipor_0.4.7            
## [52] beeswarm_0.4.0          zip_2.3.1               httpuv_1.6.15          
## [55] clipr_0.8.0             glue_1.8.0              promises_1.3.2         
## [58] grid_4.4.1              checkmate_2.3.1         ggformula_0.12.0       
## [61] generics_0.1.3          gtable_0.3.6            labelled_2.13.0        
## [64] tzdb_0.4.0              hms_1.1.3               xml2_1.3.6             
## [67] car_3.1-3               utf8_1.2.4              pillar_1.9.0           
## [70] yulab.utils_0.1.4       later_1.3.2             lattice_0.22-6         
## [73] tidyselect_1.2.1        fontLiberation_0.1.0    fontBitstreamVera_0.1.1
## [76] svglite_2.1.3           crul_1.4.2              xfun_0.48              
## [79] mosaicCore_0.9.4.0      stringi_1.8.4           lazyeval_0.2.2         
## [82] yaml_2.3.10             evaluate_1.0.1          httpcode_0.3.0         
## [85] gdtools_0.3.7           cli_3.6.3               xtable_1.8-4           
## [88] systemfonts_1.1.0       munsell_0.5.1           jquerylib_0.1.4        
## [91] Rcpp_1.0.13             viridisLite_0.4.2       broom.helpers_1.15.0   
## [94] ggridges_0.5.6          scales_1.3.0            crayon_1.5.3           
## [97] rlang_1.1.4